Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Abstract

The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

Adaptive neural PD controllers for mobile manipulator trajectory tracking

“…This section shows through simulation and experimental test the performance of the proposed controllers, the adaptive single neuron PD (SNPD) and multilayer network PD (MNPD). The controllers are compared against conventional PID controller, and an existing single neuron adaptive PID (SNA-PID) (Tang et al, 2020). Reference trajectories The values shown in Eq.…”

“…Additionally, the SNA-PID learning rates are tuned to η x = η y = η z = 1.0 × 10 −5 and the proportional coefficient is set to K = 2.5. The setting for SNA-PID is tuned based on Tang et al (2020).…”

“…The single neuron controllers have three inputs, which are the proportional, derivative, and integral errors. The output of the neuron represents the control action (Rivera-Meja, Léon-Rubio & Arzabala-Contreras, 2012;Jiao et al, 2018;Tang et al, 2020). The multilayer controllers consist of a network with one hidden layer and one node at the output layer.…”

“…The performance of the controllers is shown in simulation and experimental results on trajectory tracking tasks for a mobile manipulator robot. In simulations, the proposed controllers are compared against the conventional PID, and an existing single neuron adaptive PID (SNA-PID) controllers (Tang et al, 2020). In real experiments, a study on the robustness of the proposed controller under the presence of disturbances and non-modeled dynamics is presented.…”

Turbojet Thrust Augmentation through a Variable Exhaust Nozzle with Active Disturbance Rejection Control

“…Turbojet control, on the other hand, has been a subject of interest of many researchers and different methods to overcome its difficulties have been developed. For instance, H ∞ synthesis algorithms [7], single neural adaptive propositional-integral-derivative PID controllers [8] and non-linear controllers based on a linear control-loop with an exogenous non-linearity [4] have been developed to handle the process non-linearities. On the other hand, model uncertainty has been handled through model predictive control in [9,10].…”